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Internet of Things (IoT) approach is empowering smart city creativities all over the world. There is no specific tool or criteria for the evaluation of the services offered by the smart city. In this paper, a new Multilayer Fuzzy Inference System (MFIS) is proposed for the assessment of the Planet Factors of smart city (PFSC). The PFSC system is categorized into two levels. The proposed MFIS based expert system can categories the evaluation level of planet factors of the smart city into low, satisfied, or good.
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ISeCure
The ISC Int'l Journal of
Information Security
Special Issue of ISeCure Journal (pp. 51–58)
http://www.isecure-journal.org
Evaluation of Planet Factors of Smart City through Multi-layer
Fuzzy Logic (MFL)I
Areej Fatima 1, Muhammad Adnan Khan 1, Sagheer Abbas 1,, Muhammad Waqas 1,
Leena Anum 1, and Muhammad Asif 1
1Department of Computer Science, National College of Business Administration & Economics, Lahore, Pakistan
A R T I C L E I N F O.
Keywords:
IoT, Smart City, PFSC, Multilayer
Fuzzy logic.
Abstract
Internet of Things (IoT) approach is empowering smart city creativities all over
the world. There is no specific tool or criteria for the evaluation of the services
offered by the smart city. In this paper, a new Multilayer Fuzzy Inference
System (MFIS) is proposed for the assessment of the Planet Factors of smart
city (PFSC). The PFSC system is categorized into two levels. The proposed
MFIS based expert system can categories the evaluation level of planet factors
of the smart city into low, satisfied, or good.
c
2019 ISC. All rights reserved.
1 Introduction
I
nternet of Things (IoT) is the most frequently dis-
cussed worldview that imagines in the future in
which all of the things that are used in the daily rou-
tine of life must be embedded with microcontrollers,
sensors and transceivers for digital transmission and
communication, and appropriate protocol nodes. That
kind of protocol can be used to make them able to
create a great opportunity to speak with each other
and with the clients, becoming a vital portion of the
Internet [1].
However, such a varied field of the utility of IoT
for the smart city makes not only the recognition of
best solutions but also a necessity of all related util-
ity situations an alarming challenge. [
2
]. The most
important thing is IoT platform design and develop-
ment which requires a perfect solution that is called
I
The ICCMIT’19 program committee effort is highly acknowl-
edged for reviewing this paper.
Corresponding author.
Email addresses: areejfatima@lgu.edu.pk,
madnankhan@ncbae.edu.pk,dr.sagheer@ncbae.edu.pk,
waqas.ger@gmail.com,leenarehman@ncbae.edu.pk,
muhammadasif@ncbae.edu.pk
ISSN: 2008-2045 c
2019 ISC. All rights reserved.
middleware-level solution to enable the seamless in-
teroperability between machine-to-machine based ap-
plication and existing internet-based service [3].
All the more, by and large, we could state that
a smart city procedure goes for utilizing the tech-
nology to expand the personal satisfaction in ur-
ban space, both enhancing the environmental qual-
ity and conveying better administrations to the res-
idents [
4
].Transmission and information technology
are among the most important aspects used to sup-
port and change urban city to smart city [
5
]; there-
fore, a digital city is frequently utilized synonymous
and identical to a smart city. The application and
architecture of the IoT paradigm to the smart city is
principally alluring to nearby and regional organiza-
tions that may turn into the early adopters of such
advances. Accordingly, going about as catalyzers for
the appropriation of the IoT worldview on a more
extensive scale [6].
The purpose of this explanation is to talk about
a general reference structure for the plan of an ur-
ban IoT. Smart city defines the exact features and
properties of the urban city.
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52
2 Literature Review
The Internet of Things (IoT) is the interconnection
of various physical gadgets, vehicles, mobile, and dif-
ferent things implanted with hardware, programming,
sensors, actuators, and availability which empowers
these items to interface and trade information [7].
The first important factor for the smart city is sus-
tainable energy integration and consumption for an
essential multi-axial complex of smart city design and
architecture [
8
]. The aggressive growth of urban city
causes an increase in pollution and waste manage-
ment matters in most of the countries all over the
world [
9
]. For instance, Pakistan has similarity issues
since last few decades even in developed cities in-
cluding Karachi, Islamabad, and Lahore. Smart city
initiatives are aimed to create digital innovation to
make the lives of citizens batter and improve urban
living sustainably by excelling in environment and
properties, transportation and people amenities, such
as education, healthcare, and living [
10
]. To calculate
the evaluation of PFSC, we need to follow a specific
model similar to swarm intelligence that is advance
research in which we have to analyse and identify cor-
respondence algorithms that represent the behaviors
modeling techniques. By using swarm Intelligence, we
can define many algorithms that represent the behav-
iors of the swarm of flies and other organisms such
as fishes, bees or insects. When we have many inputs
and outputs, we have to use MIMO (Multiple-Input
and Multiple Outputs) technologies to improve our
quality of the system in IoT and smart city [11].
Whereas in PFSC, many other elements are in-
volved, but there is a great role of RFID technology
of novelty for traffic blocking and controlling. Sensors
play an important role for this purpose that identifies
every action by using an RFID reader. At this stage,
we have to use an intelligent system to maintain the
dynamic changes of time whenever a single action
occurs [
12
].In the current era, new cities have issues
about the urbanization, and other improve life stan-
dard. The latest research says that social networking
requires a specific and precise framework that can
help to analyze the data set of social networking sites
and sensor devices in the smart city [13].
We need a specific method in which we have to
transmit a path that identifies in different devices.
A multicarrier method that is Multi-Carrier Code
Division Multiple Access (MC-CDMA) provide the
solution to this problem [
14
]. Another important issue
is how much Multiple Input and Multiple Output
(MIMO) systems are helpful in PFSC. This issue is
much highlighted in wireless technologies. MC-CDMA
with Alamouti’s Space-Time Block Codes (STBC)
is the paramount solution of this issue [
15
]. Fuzzy
Figure 1. DFD of Proposed Methodology of PFSC
Logical Controller (FLC) that can be implemented for
prototyping panel and mathematical results of PFSC
calculations are assessed for altered configuration and
input parameters [16].
3 Proposed Methodology Multilayer
Fuzzy Logic of PFSC System
New processing strategies give fluffy rationale can be
utilized as a part of the improvement of insightful
frameworks for basic leadership, ID, design acknowl-
edgment, streamlining, and control [
17
]. Fuzzy sur-
mising guidelines will be an aid for giving a round
check to all components in urban areas. The plan is
required to encourage wellbeing, economic framework,
biological system, and energy and water utilization
other parental figures to make choices wisely. In this
manner, the IoT based framework can be utilized all
the more proficiently without trading off with the na-
ture of the administration of the framework [
18
]. Fig-
ure 1 demonstrates DFD of proposed methodology of
PFSC and Figure 2 demonstrates proposed method-
ology multilayer fuzzy interface system of PFSC.
3.1 Inputs Variables
The membership function of this system gives curve
output values between 0 and 1 and also provides a
mathematical function that offers statistical values
of input and output variables. The only first layer
(energy and mitigation factors) considered as level 1,
the rest of the layers is correspondence.
3.2 Outputs Variables
The output variables for multiple-layer of proposed
PFSC needs to be set up. First of all, we have to
evaluate the results from layer one; if the results
are satisfying the condition, then layer two will be
activated.
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53
Figure 2
. Proposed Methodology Multilayer Fuzzy Interface
System of PFSC
3.3 Member Function
Membership function represents the curve values
ranged between 0 and 1. It is a mathematical nota-
tion of input and output variables. FIS input/output
variables of EMF for layer one and layer two with
a graphical and mathematical representation of the
proposed methodology PFSC system is demonstrated
in Figure 3; where first five rows represent the input
member function while row six represents the output
member function.
3.4 Fuzzy Set Operations
The most important set operations in fuzzy are an
intersection, union and additive compliment. They
manage the essence of fuzzy logic. If there are two
fuzzy sets A and B defined on the universe X, x
X
Then the fuzzy set operation can be written as:
Intersection(AN D) = µAB(x) =
min(µA(x), µB(x))
Union(OR) = µAB(x) = max(µA(x), µB(x))
AdditiveComplement(N OT ) = µA(x)=1µB(x)
3.5 Fuzzy Propositions
A proposition is a statement that is either true or
false. There is multi-layered architecture proposed to
evaluate PFSC in two levels of layers.
Figure 3
. EMF for Layer One and Layer Two Input/Output
Variables Membership Functions Proposed Methodology PFSC
System
3.5.1 Layer Level One
Here, layer one contains 5-factor layers correspon-
dence with EMF, MWL, CR, PW, and ES. Every
layer has its member function represented by vari-
ables.
EM F =t:A×R×C=T1
All input and output variable values are mapped
from real range to probability ranges because fuzzy
expert system works on probability (range 0-1). T-
norm function of layer level one can be written as:
EM F =t: [0,1] ×[0,1] ×[0,1] =T1
Equation 1and 2convert the membership functions
of fuzzy sets of simulation, Energy and Mitigation
(EM), Material, Water and Land (MWL), Climate
Resilience (CR), Pollution and Wastage (PW), Echo
System (ES) as layer level one.
t[µA(a), µR(r)µC(c)] = min(µA(a), µR(r), µC(c))
(1)
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54
µARC(a, b, c) = t[µA(a), µR(r), µC(c)] (2)
µHTE(h, t, e) = t[µH(h), µT(t), µE(e)] (3)
µARC(a, r, c) = min(µA(a), µR(r), µC(c)) (4)
µMWPL(m, w, p, l) =
min(µM(m), µW(w), µP(p), µL(l)) (5)
Equation 4and 5represent the minimum of the in-
tersection of all sets.
3.5.2 Layer Level Two
Layer two contains five member functions respectively
EM, MWL, CR, PW, and ES. Every layer has their
member function represented by variables.
t:EM ×M W L ×CR ×P W ×ES =Lt
t: [0,1] ×[0,1] ×[0,1] ×[0,1] ×[0,1] =Lt
t[µEM (em), µM W L(mwl),
µCR (cr), µP W (pw), µE S (es)]
=min(µEM (em), µM W L(mwl),
µCR (cr), µP W (pw), µE S (es))]
µEM M W LCRP W E S
(em, mwl, cr, pw, es) =
min(µEM (em), µM W L(mwl),
µCR (cr), µP W (pw), µE S (es))
(6)
Equation 6specifies the minimum of the intersection
of all sets.
3.6 Lookup Table
The fuzzy rule base is the important element of Fuzzy
Inference System (FIS) because other components of
FIS like rules surface and rules viewer are dependent
upon fuzzy rule base. Fuzzy rule base of proposed
PFSC contains 27 rules at layer one (Only for EMF)
and denoted by Rαn, where 1n27.
Rα3= IF ES is Poor AND PW is Poor AND CR
is Satisfied AND MWL is Poor AND EM is Poor
that represents weightage of PFSC is very poor.
µp1(em, mwl, cr, pw, es) = µP oor (ES ),
µP oor(P W ), µS at(CR), µGood (M W L), µP oor (EM )
Rα300 =IF CR is Satisfaction AND MWL is Good
AND EM is Good that represents weightage of
PFSC is very Good.
µG1(em, mwl, cr, pw, es) =
µn/a(ES), µn/a(P W ), µSat(C R),
µGood(M W L), µGood(E M )
µSC P F (p1, s1, g1) =
max(min(1,0.09 0
0.1),0)|
max(min(00.14
0.1,0.06 0
0.1),0)|
max(min(00.49
0.1,1),0)
Fuzzy rule PFSC System (Layer Two) contains 300
rules denoted by Rαn, where 1n300.
3.7 Mamdani Implications
The fuzzy IF-THEN rules
Rα27
,
Rβ144
,
Rγ27
,
Rπ120
,
and
Rφ27
are with membership functions of the first
level layer as follow:
Rαn=µEM F (a,r,c)
A1 = if use of a,r,c are poor condition
Then the fuzzy output must be
A2 = use of Energy and Mitigation Factors (EMF)
must be poor for first level layer
µA1(a, r, c) = µpoor(a)µpoor (r)µpoor (c)
or we can write it as:
µA1(a, r, c) = {(1000a
0.1)(900r
0.1)(09c
0.1)
0
}
or
{
(1000a)(900r)(09c)
(0.1)3
0
}
The output of fuzzy are as follow:
µA2(t) = µP oor(t)
µA2(t) = {
(0.15t)
(0.1)
0
}
ift =[0,1]
Similarly, we can get rest of layer
Rα140 , Rβ27 , Rγ120
,
and Rπ27 .
Rβ1300
for the second level layer with membership
functions are written as
Rβ1300 =µSC P F (em, mwl, cr, pw, es))
Suppose:
B1 = IF ES is Poor AND PW is Good, AND
CR is Poor AND MWL is Poor AND EM is Poor,
Then fuzzy output must be
B2 = weight of PFSC is very poor
Then we may calculate the fuzzy product of
simulation:
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55
µSC P F (em, mwl, cr, pw, es) = µpoor(E S),
µpoor(M W L), µpoor (CR), µpoor (P W ), µpoor(EM )
or we can write it as
µB1(em, mwl, cr, pw, es) =
{((0.15es)
(0.1) )((0.4mwl)
(0.1) )((0.3cr)
(0.1) )((0.38pw)
(0.1) )((0.36em)
(0.1) )
0
}
={((0.15es)(0.4mwl)(0.3cr)(0.38pw)(0.36em)
(0.1)5)
0
}
if es, mwl, cr, pw, em =[0,1]
The output of fuzzy are:
µB2(t) = {((0.09t)
(0.1) )
0
}
ift =[0,1]
3.8 Fuzzy Interface Engine
The process of combining the fuzzy IF-THEN rules
from the fuzzy rule base into a mapping from a fuzzy
input set to fuzzy output based fuzzy logic principle
is called the fuzzy inference engine. The main com-
ponent of fuzzy inference is membership functions,
fuzzy logic operators, and if-then rules. All rules in
the fuzzy rule base are combined into a single fuzzy
relation that lies under the inner product on input
universes of discourse, which is then viewed as a sin-
gle fuzzy IF-THEN rule. A reasonable operator for
joining the rules is union.
Layer one IF-THEN fuzzy represent as:
Rαn=An×Rn×Cn
µARC(a, r, c) = µA(a)µR(r)µC(c)(7)
Interpreted as a single fuzzy relation defined by:
R27 =t2
n=17Rn
a
Suppose
π
,
λ
, and
ψ
be any three arbitrary fuzzy
sets and are also input and output to the fuzzy in-
ference engine, respectively. To view
R27
as a single
fuzzy IF-THEN rule and using the generalized modus
ponens:
µP oorSatf Accept (ϕ) = Supλ(A,R,C)
t[µλ(A, R, C), µR27 (A, R, C)]
Using product inference engine format we have:
µϕ(T) = max06x627[Supa,r,cU (µA,R,C (a, r, c))
(q27
k=1(µak,rk,ck(ak, rk, ck))µϕx(T))]
Layer Two IF-THEN fuzzy represent as:
Figure 4
. Rule Surface of Proposed PFSC based on Climate
Resilience and Energy Factors
Rβn=En×Mn×Cn×Pn×EM n
µEMCPEM (e, m, c, p, em) =
µE(e)µM(m)µC(c)µP(p)µEM (em)
Interpreted as a single fuzzy relation for layer two
is defined by:
R300 =t300
n=1Rn
a
Interpreted as a single fuzzy relation defined as:
Ran
3.9 De-Fuzzifier
For discrete membership function, the defuzzified
value denoted as xusing COG is defined as:
x=Pn
i=1 xiµ(xi)
Pn
i=1 µ(xi)
where
xi
indicates the sample element,
µ
(
xi
)is the
membership function, and n represents the number
of elements in the sample. The center of gravity de-
fuzzifier specifies
x
and
x∗∗
, as the center of area
covered by a member function of A and B.
Center of Gravity (COG) for layer one and two are
computed as represented in equation 8and 9, re-
spectively.
x=R
v
µa(
v
)d
v
Rµa
(v)
d
v
Centerof gravity(COG)f orLayer2
(8)
x=Rb(B)dB
Rµb(B)dB (9)
Where Ris the conventional integral.
Figure 4 represents the 3D view of rule surface of
proposed PFSC only based on CR and EM. It ob-
served that PFSC weightage is Good (Yellow shade)
if CR is
0.6 (60%), and PFSC weightage is Satisfy
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56
Figure 5
. Rule Surface of Proposed PFSC System Based on
MWL and ES
Figure 6
. Rule Surface of Proposed PFSC System Based on
PW and MWL
Figure 7
. Layer Two Lookup Diagram for Proposed PFSC
System (Poor)
(Greenish Shade) when EM Simulation is lain be-
tween 0.15 and 0.4 (15% and 40%). Otherwise, PFSC
weightage is Weak or Poor (Bluish Shade).
Figure 5 and Figure 6 represent the 3D view
by prevailing different input parameter values. The
former is based on MWL and ES, while the latter
is based on PW and MWL. Yellow, Greenish, and
Bluish shade represents weightage Strong, Good, and
Poor respectively.
4 Simulation Results
For layer two simulation results, the five-member
function has 300 inputs and outputs representing
layer-2’s, and results show that what weight is for
PFSC. Since layer two evaluation is final, it indicates
the actual strength and feasible planet factors for
the smart city. Figure 7 shows that if the values
ES, EMF, and CR are in a lower range while PW
and MWL are in a high range, then PFSC is not an
efficient condition. Figure 8 shows that if the values
ES and EMF are not available while CR is in an
average range, PW and MWL are in a high range,
then PFSC is an efficient condition. Figure 9 shows
that if the values ES and EMF are not available while
Figure 8
. Layer Two Lookup Diagram for Proposed PFSC
System (Satisfy)
Figure 9
. Layer Two Lookup Diagram of Proposed PFSC
System (Good)
CR is in an average range, PW and MWL are in a
high range, then PFSC is an efficient condition.
5 Conclusion
The connected approach for computing smart indi-
cator loads features, for choosing such criteria, with
the significance of the decision maker’s subjectivity.
Indeed, doling out the heaviness of a savvy pointer re-
garding another smart marker for planet factors, each
decision maker is conveyed to reason in a less target
way [
19
]. If there should arise an occurrence of a real
city, the foundation of right qualities requires the mas-
ter’s commitment to the different picked fields [
20
].
This research has opened doors of innovation for dif-
ferent major projects in Pakistan. Gwadar is Pak-
istan’s biggest up-coming project, started a few years
ago. Although the first phase of the Gwadar port has
been established, and billions of dollars have been
invested in infrastructure, yet there is no evaluation
and estimation weight for supporting and validity for
a smart city. By using this model, we can get a good
estimation of the evaluation of this city for the PFCS
system. Consequently, it will be conceivable to fur-
nish the arrangement creator with data for "ready
consultation" to give him the data that permit to
visit and to gauge the impacts of his mediation. The
proposed creative framework results in an undeni-
ISeCure
57
ably expanded understanding and essential use, for
both the chiefs and the resident, without respecting
abilities and individual subjectivity.
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Areej Fatima
is currently work-
ing as a lecturer at the department
of computer science, Lahore Garri-
son University, Lahore, Pakistan. She
is doing a PhD at the School of
computer science, NCBA&E, Lahore,
Pakistan. She completed her master
of computer sciences from the department of com-
puter science NCBA&E, Lahore, Pakistan. Areej’s
research interests primarily include cloud computing,
IoT, intelligent agents, image processing and cogni-
tive machines with various publications in interna-
tional journals and conferences.
ISeCure
58
Muhammad Adnan Khan
is cur-
rently working as an assistant pro-
fessor at the School of computer sci-
ence, NCBA&E, Lahore, Pakistan.
He completed his PhD at ISRA Uni-
versity, Pakistan. Before joining the
NCBA&E, Khan has worked in var-
ious academic and industrial roles in Pakistan. He
has been teaching graduate and undergraduate stu-
dents in computer science and engineering for the
past ten years. Presently he is guiding 4 PhD schol-
ars and 3 M.Phil. Scholars. He has published about
109 research articles in international journals as well
as reputed international conferences. Khan’s research
interests primarily include MUD, channel estimation
in multi-carrier communication systems using soft
computing, image processing and medical diagnosis
with various publications in journals and conferences
of international repute.
Sagheer Abbas
is currently work-
ing as an assistant professor at
the School of computer science,
NCBA&E, Lahore, Pakistan. He
completed his PhD at the School of
computer science, NCBA&E, Lahore,
Pakistan. He completed his M.Phil.
In computer science at the School of computer science,
NCBA&E, Lahore, Pakistan. He has been teaching
graduate and undergraduate students in computer sci-
ence and engineering for the past eight years. He has
published about 48 research articles in international
journals as well as reputed international conferences.
Sagheer’s research interests primarily include cloud
computing, IoT, intelligent agents, image processing
and cognitive machines with various publications in
international journals and conferences.
Muhammad Waqas
has com-
pleted M.Phil from the department
of computer science, of NCBA&E,
Lahore, Pakistan. Waqas’s research
interests primarily include cloud com-
puting, IoT, intelligent agents and
cognitive machines with various pub-
lications in international journals and conferences.
Leena Anum
is an assistant profes-
sor at the School of business adminis-
tration at NCBA&E, Lahore. She did
her PhD in business administration
in March 2019. Her research interests
include various cross-disciplinary ar-
eas ranging from knowledge manage-
ment, knowledge-based systems to smart cities.
Muhammad Asif
is currently
working as a lecturer at the depart-
ment of computer science, NCBA&E,
Lahore, Pakistan. He completed his
master of computer sciences from
the department of computer science
NCBA&E, Lahore, Pakistan. Asif´ s
research interests primarily include fuzzy system,
cloud computing, IoT, and smart city with various
publications in conferences of international repute.
ISeCure
... Networks are becoming heterogeneous and it is a challenge to automate the flow of traffic and control a large number of devices [2]. In general, the production network uses many devices, operates a number of procedures and supports a number of applications [3,4]. As an example, different types of modules have been used in wireless networks, such as ZigBee, WiMAX, IEEE 802.11 ac/ad, Bluetooth, and Long-Term Evolution (LTE), with specific communication range, strength, and operating mechanisms and numerous communication technologies. ...
... The Internet of Things (IoT) transforms communities around the world into "smart cities" by creating a new way of living in urban areas [15]. Main advantages include improved safety, health, improved environment for education and housing, energy consumption, better climate and ecosystem efficiency, green economy and better employment [3]. Although the central knowledge of smart cities has been around for more than a decade, the definition has improved a lot since its initial release, it is now expected to fundamentally change city life as a number of important facilitators. ...
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In recent years, the infrastructure, instruments, and resources of network systems are becoming more complex and heterogeneous, with the rapid development of current internet and mobile communication technologies. In order to efficaciously prepare, control, hold and optimize networking systems, greater intelligence needs to be deployed. However, due to the inherently dispensed characteristic of conventional networks, Machine Learning (ML) techniques are hard to implement and deployed to govern and operate networks. Software-Defined Networking (SDN) brings us new possibilities to offer intelligence in the networks. SDN's characteristics (e.g., logically centralized control, global network view, software-based site visitor analysis, and dynamic updating of forwarding rules) make it simpler to apply machine learning strategies. Various perspectives of fiber-optic communications including fiber nonlinearity coverage, optical performance checking, cognitive shortcoming detection/anticipation, and arranging and improvement of software-defined networks are examined in Machine Learning (ML) applications. This research paper has presented an imaginative framework concept called Intelligent Software Defined Network (ISDN) for Cognitive Routing Optimization (CRO) using Deep Extreme Learning Machine (DELM) approach (ISDN-CRO-DELM) in light of the new challenges in the development and operation of communication systems, and capturing motivation from how living creatures deal with difficulty and usability. The proposed methodology develops around the planned applications of progressive DELM methods and, specifically, probabilistic generative models for framework wide learning , demonstrating, improvement, and information description. Furthermore, This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1270 CMC, 2021, vol.67, no.1 ISDN-CRO-DELM, suggest to integrate this learning framework with the ISDN for CRO and reconfiguration approaches at the system level. MATLAB 2019a is used for DELM simulation and superior results show the effectiveness of the proposed framework.
... Most of the approaches have been used while employing and constructing several smart as well as intelligent frameworks like machine learning approaches [11][12][13][14][15][16][17][18][19][20][21]37], Fuzzy Inference systems [22][23][24][25], Particle Swarm Optimization (PSO) [26], Fusion based approaches [27][28][29][30][31][32] ,cloud computing [33][34][35][36], transfer learning [38] and MapReduce that may provide assistance in designing emerging solutions for the rising challenges in designing smart cloud-based monitoring management systems. ...
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Today's healthcare data is fragmented and not properly kept in one place. However, some records aren't even made digital, which makes research uncertain. Mutually the number of dubious medications on the market and the volume of false insurance claims are rising. Additionally, patients are demanding an infrastructure that offers them the majority of the controls as they become more aware of all the factors and increasingly demanding of patient-centric models. In this research, a blockchain-established intelligent healthcare system is explored that would be able to give real-time data access in a transparent, traceable, and reliable way. Several industries, including finance, manufacturing, e-commerce, education, etc., have been altered by the Blockchain, and it is now entering the healthcare sector. The Blockchain is a distributed technology built on a network of peer-to-peer computers that are not governed by a single central authority. Blocks are the units used to represent data on the blockchain network. Therefore, the advancement of society through healthy and effective healthcare can benefit from systems like blockchain technology.
... Most of the approaches have been used while employing and constructing several smart as well as intelligent frameworks like machine learning approaches [13][14][15][16][17][18][19][20][21][22][23] ,Fuzzy Inference systems [24][25][26][27], Particle Swarm Optimization (PSO) [28], Fusion based approaches [29][30][31][32][33][34][35][36][37][38][39], transfer learning [40] and MapReduce that may provide assistance in designing emerging solutions for the rising challenges in designing smart cloud-based monitoring management systems. ...
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The academic world of today is a complicated and highly competitive one. It is hard to evaluate student performance and provide high-quality education, as well as establish ways for assessing student achievement. In attempt to face the difficulties that students face while pursuing their education, educational institutions must establish student prevention strategies. Students' performance may be predicted using a Decision Tree (DT) model created in this study. The advancement of the learning environment is greatly aided by educational data mining, which contributes modern approaches, strategies, and applications. Students' learning environments may now be better understood via the use of machine learning and data mining approaches that use educational data. Students at trouble and students who drop out may be predicted using a variety of machine learning approaches, including K-Nearest Neighbor, Support Vector Machine, Logistic Regression (LR), and Naive Bayes (NB) algorithms. By using the DT technique to forecast student performance, this suggested model may be able to perform better.
... Deep & Machine learning arose over the last two decades from the increasing capacity of computers to process large amounts of data. Computational Intelligence approaches like Swarm Intelligence [16], Evolutionary Computing [17] like Genetic Algorithm [18], Neural Network [19], Deep Extreme Machine learning [20] and Fuzzy system [21][22][23][24][25][26][27] are strong candidate solution in the field of smart city [28][29][30], smart health [31][32][33], and wireless communication [34,35], etc. ...
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In the agricultural industry, rice infections have resulted in significant productivity and economic losses. The infections must be recognized early on to regulate and mitigate the effects of the attacks. Early diagnosis of disease severity effects or incidence can preserve production from quantitative and qualitative losses, reduce pesticide use, and boost ta country's economy. Assessing the health of a rice plant through its leaves is usually done as a manual ocular exercise. In this manuscript, three rice plant diseases: Bacterial leaf blight, Brown spot, and Leaf smut, were identified using the Alexnet Model. Our research shows that any reduction in rice plants will have a significant beneficial impact on alleviating global food hunger by increasing supply, lowering prices, and reducing produc-tion's environmental impact that affects the economy of any country. Farmers would be able to get more exact and faster results with this technology, allowing them to administer the most acceptable treatment available. By Using Alex Net, the proposed approach achieved a 99.0% accuracy rate for diagnosing rice leaves disease.
... Deep & Machine learning arose over the last two decades from the increasing capacity of computers to process large amounts of data. Machine learning approaches like Swarm Intelligence [45], Evolutionary Computing [46] like Genetic Algorithm [47], Neural Network [48], Deep Extreme Machine learning [49] and Fuzzy system [50][51][52][53][54][55][56] are strong candidate solution in the field of smart city [57][58][59], smart health [60,61], and wireless communication [62,63], etc. ...
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Progress in understanding multisensory integration in human have suggested researchers that the integration may result into the enhancement or depression of incoming signals. It is evident based on different psychological and behavioral experiments that stimuli coming from different perceptual modalities at the same time or from the same place, the signal having more strength under the influence of emotions effects the response accordingly. Current research in multisensory integration has not studied the effect of emotions despite its significance and natural influence in multisensory enhancement or depression. Therefore, there is a need to integrate the emotional state of the agent with incoming stimuli for signal enhancement or depression. In this study, two different neural network-based learning algorithms have been employed to learn the impact of emotions on signal enhancement or depression. It was observed that the performance of a proposed system for multisensory integration increases when emotion features were present during enhancement or depression of multisensory signals.
... Deep & Machine learning arose over the last two decades from the increasing capacity of computers to process large amounts of data empowered with cloud computing [27,28]. Computational Intelligence approaches like Swarm Intelligence [29], Evolutionary Computing [30] like Genetic Algorithm [31], Neural Network [32], Deep Extreme Machine learning [33] and Fuzzy system [34][35][36][37][38] are strong candidate solutions in the field of the smart city [39][40][41], smart health empowered with cloud computing [42,43], and wireless communication [44,45,46], etc. ...
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Cloud computing is providing IT services to its customer based on Service level agreements (SLAs). It is important for cloud service providers to provide reliable Quality of service (QoS) and to maintain SLAs accountability. Cloud service providers need to predict possible service violations before the emergence of an issue to perform remedial actions for it. Cloud users' major concerns; the factors for service reliability are based on response time, accessibility , availability, and speed. In this paper, we, therefore, experiment with the parallel mutant-Particle swarm optimization (PSO) for the detection and predictions of QoS violations in terms of response time, speed, accessibility, and availability. This paper also compares Simple-PSO and Parallel Mutant-PSO. In simulation results, it is observed that the proposed Parallel Mutant-PSO solution for cloud QoS violation prediction achieves 94% accuracy which is many accurate results and is computationally the fastest technique in comparison of conventional PSO technique.
... Keywords: Wireless internet of sensor networks; machine learning; deep extreme learning machine; artificial intelligence; data fusion This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 3400 CMC, 2022, vol.70, no.2 general, a production network uses many devices, runs several protocols, and supports several applications [2]. Data transmission has been growing exponentially in the world recently with the rapid evolution of smart devices and network technologies (for example, cloud computing and virtualization of networks) [3]. ...
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In recent years, the infrastructure of Wireless Internet of Sensor Networks (WIoSNs) has been more complicated owing to developments in the internet and devices' connectivity. To effectively prepare, control, hold and optimize wireless sensor networks, a better assessment needs to be conducted. The field of artificial intelligence has made a great deal of progress with deep learning systems and these techniques have been used for data analysis. This study investigates the methodology of Real Time Sequential Deep Extreme Learning Machine (RTS-DELM) implemented to wireless Internet of Things (IoT) enabled sensor networks for the detection of any intrusion activity. Data fusion is a well-known methodology that can be beneficial for the improvement of data accuracy, as well as for the maximizing of wireless sensor networks lifespan. We also suggested an approach that not only makes the casting of parallel data fusion network but also render their computations more effective. By using the Real Time Sequential Deep Extreme Learning Machine (RTS-DELM) methodology, an excessive degree of reliability with a minimal error rate of any intrusion activity in wireless sensor networks is accomplished. Simulation results show that wireless sensor networks are optimized effectively to monitor and detect any malicious or intrusion activity through this proposed approach. Eventually, threats and a more general outlook are explored.
... The Internet of Things (IoT) conveys the promise of turning neighborhoods across the globe into "smart towns" by developing a modern environment for modern living [21]. Significant benefits include improved protection, wellness, upgrading schooling and housing conditions, electricity use, habitat and biodiversity sustainability, green economy and enhancing the job structure [22]. While the fundamental principle of smart cities has been around for about 10 years, but the term has made a lot of progress since it was launched in the early days, today the notion of fundamentally altering community existence is the introduction of various important enablers, such as the Internet of Things. ...
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The fast-paced growth of artificial intelligence provides unparalleled opportunities to improve the efficiency of various industries, including the transportation sector. The worldwide transport departments face many obstacles following the implementation and integration of different vehicle features. One of these tasks is to ensure that vehicles are autonomous, intelligent and able to grow their repository of information. Machine learning has recently been implemented in wireless networks, as a major artificial intelligence branch, to solve historically challenging problems through a data-driven approach. In this article, we discuss recent progress of applying machine learning into vehicle networks for intelligent route decision and try to focus on this emerging field. Deep Extreme Learning Machine (DELM) framework is introduced in this article to be incorporated in vehicles so they can take human-like assessments. The present GPS compatibility issues make it difficult for vehicles to take real-time decisions under certain conditions. It leads to the concept of vehicle controller making self-decisions. The proposed DELM based system for self-intelligent vehicle decision makes use of the cognitive memory to store route observations. This overcomes inadequacy of the current in-vehicle route-finding technology and its support. All the relevant route-related information for the ride will be provided to the user based on its availability. Using the DELM method, a high degree of precision in smart decision taking with a minimal error rate is obtained. During investigation, it has been observed that proposed framework has the highest accuracy rate with 70% of training (1435 samples) and 30% of validation (612 samples). Simulation results validate the intelligent prediction of the proposed method with 98.88%, 98.2% accuracy during training and validation respectively.
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The objective of this paper is to calculate the time complexity of the colored camera depth map hand edge closing algorithm of the hand gesture recognition technique. It has been identified as hand gesture recognition through human-computer interaction using color camera and depth map technique, which is used to find the time complexity of the algorithms using 2D minima methods, brute force, and plane sweep. Human-computer interaction is a very much essential component of most people's daily life. The goal of gesture recognition research is to establish a system that can classify specific human gestures and can make its use to convey information for the device control. These methods have different input types and different classifiers and techniques to identify hand gestures. This paper includes the algorithm of one of the hand gesture recognition "Color camera depth map hand edge recognition" algorithm and its time complexity and simulation on MATLAB.
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Multiple-input and multiple-output (MIMO) technology is one of the latest technologies to enhance the capacity of the channel as well as the service quality of the communication system. By using the MIMO technology at the physical layer, the estimation of the data and the channel is performed based on the principle of maximum likelihood. For this purpose, the continuous and discrete fuzzy logic-empowered opposite learning-based mutant particle swarm optimization (FL-OLMPSO) algorithm is used over the Rayleigh fading channel in three levels. The data and the channel populations are prepared during the first level of the algorithm, while the channel parameters are estimated in the second level of the algorithm by using the continuous FL-OLMPSO. After determining the channel parameters, the transmitted symbols are evaluated in the 3rd level of the algorithm by using the channel parameters along with the discrete FL-OLMPSO. To enhance the convergence rate of the FL-OLMPSO algorithm, the velocity factor is updated using fuzzy logic. In this article, two variants, FL-total OLMPSO (FL-TOLMPSO) and FL-partial OLMPSO (FL-POLMPSO) of FL-OLMPSO, are proposed. The simulation results of proposed techniques show desirable results regarding MMCE, MMSE, and BER as compared to conventional opposite learning mutant PSO (TOLMPSO and POLMPSO) techniques.
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The paper explores the utilization of RFID innovation for traffic congestion and find out the blockage at any intersection of the street by utilizing RFID reader and labels as sensors. The idea behind this paper is to make the fixed and preset activity of traffic signal dynamic. The paper affords a unique method for making the signal timing proportional to the congestion on the roads at any time directly. Proposed intelligent system can maintain the dynamic timings of traffic signals by sensing the density of traffic to minimize the congestion with the help of IoT enabled sensors which provides the advanced and powerful communication technologies for the citizens.
Technical Report
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This report describes the selection of indicators for assessing smart city projects and the corresponding indicators on city level. Starting from the definition of a smart city and smart city projects, indicators have been selected that can function as Key Performance Indicators for tracking the progress towards city and project objectives. The indicators for assessing smart city projects serve to assess or evaluate single projects. They indicate the difference the project has made, by comparing the situation without the project with the situation after the implementation of the project. As such they can also serve to benchmark projects against each other. The indicators for smart cities focus on monitoring the evolution of a city towards an even smarter city. The time component -“development over the years”- is an important feature. The city indicators may be used to show to what extent overall policy goals have been reached, or are within reach. With a starting point in the smart city definition, and taking into account the wishes of cities and citizens with regard to smart city projects and indicators, the indicators are arranged in an extended triple bottom line sustainability framework, including the themes people, planet, prosperity, governance and propagation. Under the main themes sub-themes conforming with major policy ambitions have been identified. Under these sub-themes in total 99 project indicators and 76 city indicators have been selected. Not all indicators are equally suited for evaluating all types of smart city projects. Although there is a considerable body of common indicators, for specific sector projects a relevant subset of these may be used (i.e. some indicators are specifically suited for transport projects, other for building related projects, etc.). The selection was based on an inventory of 43 existing indicator frameworks for (sustainable) cities and projects. The majority of the indicators in the CITYkeys selection have been derived from existing indicator frameworks. New indicators have been suggested to fill gaps in existing frameworks, mostly related to specific characteristics of smart city projects. The CITYkeys project was funded as a 'horizontal activity' of the Smart Cities and Communities call to develop an indicator framework for smart city project evaluation and thus also support the so called Lighthouse projects also funded under the same call theme. In developing the indicator selection, CITYkeys has collaborated with TRIAGULUM, REMOURBAN and SMARTER TOGETHER lighthouse project consortia through joint workshops, phone calls and email exchange. The lighthouse projects implement tangible technological solutions that are expected to support smart city development and achieve environmentally-friendly, economically viable and socially desirable urban environments.
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Location-Based Services (LBS) in smart cities have drastically altered the way cities operate, giving a new dimension to the life of citizens. LBS relies on location of a device, where proximity estimation remains at its core. The applications of LBS range from social networking and marketing to vehicle-toeverything (V2X) communications. In many of these applications, there is an increasing need and trend to learn the physical distance between nearby devices. This article elaborates the current needs of proximity estimation in LBS and compares them against the available Localization and Proximity (LP) finding technologies (LP technologies in short). These technologies are compared for their accuracies and performance based on various different parameters, including latency, energy consumption, security, complexity, and throughput. Hereafter, a classification of these technologies, based on various different smart city applications, is presented. Finally, we discuss some emerging LP technologies that enable proximity estimation in LBS and present some future research areas.
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The advance of technologies in several areas has allowed the development of smart city applications, which can improve the way of life in modern cities. When employing visual sensors in that scenario, still images and video streams may be retrieved from monitored areas, potentially providing valuable data for many applications. Actually, visual sensor networks may need to be highly dynamic, reflecting the changing of parameters in smart cities. In this context, characteristics of visual sensors and conditions of the monitored environment, as well as the status of other concurrent monitoring systems, may affect how visual sensors collect, encode and transmit information. This paper proposes a fuzzy-based approach to dynamically configure the way visual sensors will operate concerning sensing, coding and transmission patterns, exploiting different types of reference parameters. This innovative approach can be considered as the basis for multi-systems smart city applications based on visual monitoring, potentially bringing significant results for this research field.
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Multicarrier systems like the multicarrier code division multiple access (MC-CDMA) systems are designed for maximum usability of available bandwidth. We use the MC-CDMA system with Alamouti's space time coding in this paper. We propose the genetic algorithm (GA) in order to calculate MC-CDMA receiver weights with two variation schemes. The proposed schemes reduce receiver complexity. The bit error rate and convergence rate are also improved by increasing the number of genes and chromosomes of the GA in both schemes as compared with conventional LMS based receivers of the MC-CDMA system. This is verified via simulations.
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The theory of fuzzy logic is based on the notion of relative graded membership, as inspired by the processes of human perception and cognition. Lotfi A. Zadeh published his first famous research paper on fuzzy sets in 1965. Fuzzy logic can deal with information arising from computational perception and cognition, that is, uncertain, imprecise, vague, partially true, or without sharp boundaries. Fuzzy logic allows for the inclusion of vague human assessments in computing problems. Also, it provides an effective means for conflict resolution of multiple criteria and better assessment of options. New computing methods based on fuzzy logic can be used in the development of intelligent systems for decision making, identification, pattern recognition, optimization, and control. Fuzzy logic is extremely useful for many people involved in research and development including engineers (electrical, mechanical, civil, chemical, aerospace, agricultural, biomedical, computer, environmental, geological, industrial, and mechatronics), mathematicians, computer software developers and researchers, natural scientists (biology, chemistry, earth science, and physics), medical researchers, social scientists (economics, management, political science, and psychology), public policy analysts, business analysts, and jurists. Indeed, the applications of fuzzy logic, once thought to be an obscure mathematical curiosity, can be found in many engineering and scientific works. Fuzzy logic has been used in numerous applications such as facial pattern recognition, air conditioners, washing machines, vacuum cleaners, antiskid braking systems, transmission systems, control of subway systems and unmanned helicopters, knowledge-based systems for multiobjective optimization of power systems, weather forecasting systems, models for new product pricing or project risk assessment, medical diagnosis and treatment plans, and stock trading. Fuzzy logic has been successfully used in numerous fields such as control systems engineering, image processing, power engineering, industrial automation, robotics, consumer electronics, and optimization. This branch of mathematics has instilled new life into scientific fields that have been dormant for a long time. Thousands of researchers are working with fuzzy logic and producing patents and research papers. According to Zadeh’s report on the impact of fuzzy logic as of March 4, 2013, there are 26 research journals on theory or applications of fuzzy logic, there are 89,365 publications on theory or applications of fuzzy logic in the INSPEC database, there are 22,657 publications on theory or applications of fuzzy logic in the MathSciNet database, there are 16,898 patent applications and patents issued related to fuzzy logic in the USA, and there are 7149 patent applications and patents issued related to fuzzy logic in Japan. The number of research contributions is growing daily and is growing at an increasing rate. Zadeh started the Berkeley Initiative in Soft Computing (BISC), a famous research laboratory at University of California, Berkeley, to advance theory and applications of fuzzy logic and soft computing. The objective of this special issue is to explore the advances of fuzzy logic in a large number of real-life applications and commercial products in a variety of fields. Although fuzzy logic has applications in a number of different areas, it is not yet known to people unfamiliar with intelligent systems how it can be applied in different products that are currently available in the market. For many people, the engineering and scientific meaning of the word fuzzy is still fuzzy. It is important that these people understand where and how fuzzy logic can be used.
Conference Paper
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The demand for the wireless communication is increasing enormously. Multiple Input and Multiple Output (MIMO) systems are helpful in this regard. The Multicarrier systems are designed with the combination of different space time coding techniques for fulfilling this demand. Multicarrier Code Division Multiple Access (MC-CDMA) with Alamouti's Space Time Block Codes (STBC) is one of them. The Genetic Algorithm (GA) is used to provide assistance in order to optimize the weights of MC-CDMA receiver. This receiver has better convergence rate than simple LMS receivers. The Bit Error Rate (BER) is also comparable at low and high Signal to Noise Ratio (SNR).
Chapter
The Internet of Things concept arises from the need to manage, automate, and explore all devices, instruments and sensors in the world. In order to make wise decisions both for people and for the things in IoT, data mining technologies are integrated with IoT technologies for decision making support and system optimization. Data mining involves discovering novel, interesting, and potentially useful patterns from data and applying algorithms to the extraction of hidden information. Data mining is classified into three different views: knowledge view, technique view, and application view. The challenges in the data mining algorithms for IoT are discussed and a suggested big data mining system is proposed.
Conference Paper
With the advent of Smart City, quality of life is bound to be better. But huge need arises to provide proper health-care services as the population is increasingly becoming urban-centric worldwide. The need-gap may be augmented with the help of modern technologies. Providing remote health-care services is a step forward. Successful diagnosis of health problems requires continuous monitoring of several health parameters. Health monitoring devices are power constrained and with limited communication capability. The devices are equipped with powerful microprocessors which are capable enough to take intelligent decisive actions by processing the received data. In order to prevent faster energy dissipation and constrained communication, selective data collection is an option. In this paper, a fuzzy assisted data gathering and alert scheme is proposed for healthcare services. Thus unnecessary waste of energy by transmission of unnecessary information is avoided. We have implemented it using Arduino and eHealth sensor kit.